2 citations · 6 across the 17 of their papers we have counts for
18 papers
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
Yuting Liu, Wei Wu, Jianzhe Zhao +1
Natural language user preferences provide an interpretable interface for LLM personalization. However, universal preference summaries often contain information irrelevant to a part…
ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
Chu Zhao, Enneng Yang, Yuting Liu +2
Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels constructed by majority voting. To reduc…
Text as a Universal Interface for Transferable Personalization
Yuting Liu, Jian Guan, Jia-Nan Li +4
We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yie…
RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation
Run Ling, Wenji Wang, Yuting Liu +12
Personalized image generation is crucial for improving the user experience, as it renders reference images into preferred ones according to user visual preferences. Although effect…
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Data augmentation has become a promising method of mitigating data sparsity in sequential recommendation. Existing methods generate new yet effective data during model training to…
Hard Negative Sampling via Large Language Models for Recommendation
Chu Zhao, Enneng Yang, Yuting Liu +2
Hard negative sampling improves recommendation performance by accelerating convergence and sharpening the decision boundary. However, most existing methods rely on heuristic strate…
Personalized Text Generation with Contrastive Activation Steering
Jinghao Zhang, Yuting Liu, Wenjie Wang +4
Personalized text generation aims to infer users' writing style preferences from their historical texts and generate outputs that faithfully reflect these stylistic characteristics…
Augmenting Sequential Recommendation with Balanced Relevance and Diversity
Yizhou Dang, Jiahui Zhang, Yuting Liu +5
By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on au…
Self-supervised Hierarchical Representation for Medication Recommendation
Yuliang Liang, Yuting Liu, Yizhou Dang +5
Medication recommender is to suggest appropriate medication combinations based on a patient's health history, e.g., diagnoses and procedures. Existing works represent different dia…
Data Augmentation for Sequential Recommendation: A Survey
Yizhou Dang, Enneng Yang, Yuting Liu +4
As an essential branch of recommender systems, sequential recommendation (SR) has received much attention due to its well-consistency with real-world situations. However, the wides…
CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation
Yuting Liu, Jinghao Zhang, Yizhou Dang +5
Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating co…
Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
Chu Zhao, Enneng Yang, Yuliang Liang +5
Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. Ho…
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
Pengxiang Lan, Enneng Yang, Yuting Liu +3
Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, w…
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
Yuting Liu, Yizhou Dang, Yuliang Liang +4
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (…
Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang +4
Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted t…
Stealthy Attack on Large Language Model based Recommendation
Jinghao Zhang, Yuting Liu, Qiang Liu +3
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS). However, while these systems have flourished, the…
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
Yuting Liu, Enneng Yang, Yizhou Dang +5
Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research ha…
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Zichen Yuan, Qi Shen, Bingyi Zheng +3
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia. Generally, the information from each modality exhibits distinct representations…